A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex

Balsor, Justin L.; Ahuja, Dezi; Jones, David; Murphy, Kathryn

doi:10.3389/fncel.2020.00245

Cited by 2 publications

(9 citation statements)

References 101 publications

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“…Thus, this visualization shows the mean expression for the 23 proteins in the 6 clusters, but it is still challenging to derive what differentiates the clusters. To address this, we applied our recently developed workflow (Balsor et al, 2019(Balsor et al, , 2020) that includes dimension reduction, identification of features and the construct of a plasticity phenotype visualization to characterize the development of the human visual cortex. This workflow is described in detail in a previous publication (Balsor et al, 2020).…”

Section: Application Of Robust Sparse K-means Clustering Clusters To Study Human Visual Cortex Developmentmentioning

confidence: 99%

“…The range of trajectories highlights the need for high-dimensional analyses to capture the complexity of this development. To help describe when the expression level of a protein in a cluster was above or below the overall mean, we implemented the over-representation analysis (ORA_phenotype function) described previously ( Balsor et al, 2020 ; Figure 8B ). Briefly, for each protein, a normal distribution was simulated using the mean and standard deviation of the expression values for all samples.…”

Section: Application Of Robust Sparse K -Means Clustering Clusters To Study Human Visual Cortex Developmentmentioning

confidence: 99%

“…To address this, we applied our recently developed workflow ( Balsor et al, 2019 , 2020 ) that includes dimension reduction, identification of features and the construct of a plasticity phenotype visualization to characterize the development of the human visual cortex. This workflow is described in detail in a previous publication ( Balsor et al, 2020 ).…”

Section: Application Of Robust Sparse K -Means Clustering Clusters To Study Human Visual Cortex Developmentmentioning

confidence: 99%

“…Moreover, clusters encompassed a range of ages like a series of overlapping waves illustrating that chronological- and brain-age have a complex relationship. In addition, a recently developed workflow to create plasticity phenotypes ( Balsor et al, 2020 ) was applied to the clusters and revealed neurobiologically relevant features that identified how the human visual cortex changes across the lifespan. These methods can help address the growing demand for multimodal integration, from molecular machinery to brain imaging signals, to understand the human brain’s development.…”

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confidence: 99%

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A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain

Balsor

Arbabi²,

Singh³

et al. 2021

Front. Neurosci.

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Studying the molecular development of the human brain presents unique challenges for selecting a data analysis approach. The rare and valuable nature of human postmortem brain tissue, especially for developmental studies, means the sample sizes are small (n), but the use of high throughput genomic and proteomic methods measure the expression levels for hundreds or thousands of variables [e.g., genes or proteins (p)] for each sample. This leads to a data structure that is high dimensional (p ≫ n) and introduces the curse of dimensionality, which poses a challenge for traditional statistical approaches. In contrast, high dimensional analyses, especially cluster analyses developed for sparse data, have worked well for analyzing genomic datasets where p ≫ n. Here we explore applying a lasso-based clustering method developed for high dimensional genomic data with small sample sizes. Using protein and gene data from the developing human visual cortex, we compared clustering methods. We identified an application of sparse k-means clustering [robust sparse k-means clustering (RSKC)] that partitioned samples into age-related clusters that reflect lifespan stages from birth to aging. RSKC adaptively selects a subset of the genes or proteins contributing to partitioning samples into age-related clusters that progress across the lifespan. This approach addresses a problem in current studies that could not identify multiple postnatal clusters. Moreover, clusters encompassed a range of ages like a series of overlapping waves illustrating that chronological- and brain-age have a complex relationship. In addition, a recently developed workflow to create plasticity phenotypes (Balsor et al., 2020) was applied to the clusters and revealed neurobiologically relevant features that identified how the human visual cortex changes across the lifespan. These methods can help address the growing demand for multimodal integration, from molecular machinery to brain imaging signals, to understand the human brain’s development.

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Section: Application Of Robust Sparse K-means Clustering Clusters To Study Human Visual Cortex Developmentmentioning

confidence: 99%

Section: Application Of Robust Sparse K -Means Clustering Clusters To Study Human Visual Cortex Developmentmentioning

confidence: 99%

Section: Application Of Robust Sparse K -Means Clustering Clusters To Study Human Visual Cortex Developmentmentioning

confidence: 99%

mentioning

confidence: 99%

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A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain

Balsor

Arbabi²,

Singh³

et al. 2021

Front. Neurosci.

Self Cite

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“…They can also be used for differential gene expression analysis to highlight which features are enriched during different stages. The top weighted proteins or genes are also useful for creating a phenotype that characterize higher dimensional changes occuring different stages of the lifespan (Balsor et al, 2020) .…”

Section: Proclus : the Proclus Clustering Methods Was Implemented In Rmentioning

confidence: 99%

A Practical Guide to Sparse K-Means Clustering for Studying Molecular Development of the Human Brain

Balsor

Arbabi

Ahuja³

et al. 2021

Preprint

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Studying the molecular development of the human brain presents unique challenges for selecting the best data analysis approach. The rare and valuable nature of human postmortem brain samples, especially for studies examining development, means that those studies have small sample sizes (n) but often include measurements (p) for a large number of genes or proteins for every sample. Thus, most of those data sets have a structure that is p >> n, which introduces the problem of sparsity. Here we present a guide to analyzing human brain development data by focusing on sparsity-based clustering methods developed for small sample sizes. We test different methods and identify an application of sparse K-means clustering called Robust Sparse K-means Clustering (RSKC) that does a good job revealing clusters of samples that reflect lifespan stages from birth to aging. The algorithm adaptively selects a subset of the genes or proteins that contributes to generating clusters of samples that are spread across the lifespan. This approach addresses a problem in current studies that were unable to identify postnatal clusters. The guide illustrates that careful selection of the clustering method is essential to reveal meaningful aspects of human brain development.

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A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex

Cited by 2 publications

References 101 publications

A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain

A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain

A Practical Guide to Sparse K-Means Clustering for Studying Molecular Development of the Human Brain

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